Research On Computation Technology Of Biochemical Network

With the human genome project's basic completion, scientific research of life indicates that it has entered a "post-genomic" era. In the post-genome era facing a major challenge is how to reveal the phenomenon of life at the whole level, which arising from interactions among DNA, RNA, proteins and small molecules of various biological systems. Under this background systems biology are proposed, which is a new emerging interdisciplinary and its goal lies in understanding the biological system at system level. Because of inherent complexity of biological system to successfully carry out research of systems biology, we must use mathematical modeling and computer simulation methods for the inherent complexity of biological systems. Modeling and simulation of organisms and cells is very difficult. Several reasons are: Firstly, the inherent complexity of biological system and the constraints of the biological experiment technology, which causes the knowledge and the empirical datum is insufficient. Secondly, the study of molecular randomness of the biological systems and how the process from molecular behaviors in micro to the macro complex phenomenon become a huge challenge. This dissertation, the method of combining multi-source data integration and agent technology to study biochemical network, the main contents include:1. Data integration is very important and helps research on systems biology, this dissertation proposes a data integration method with biochemical network model as central and construct a data platform (BioDB), which faces to specific biological issues and integrates of biological databases related to the selected specific issues. We construct the data integration system with biochemical network model as central, and the others include the related biological databases, literature knowledge, expertise, experimental data and simulated data. Experiments showed that our BioDB provides an effective data platform for reconstruction of metabolic network, making reconstruction not only have better results, but also with rapid and efficient performances.2. For the problem of biological data standards cannot share their applications, this dissertation proposes a data conversion method among several biological stan-dards(BioBridge), which provide a bridge for several biological data standards and can share these standards and their applications. The efficiency of data access is an important issue of data federation. This dissertation proposes a limited history based multi-LRU web cache replacement algorithm and constructs a data federal system (LinkDB) with web cache, which effectively improving the efficiency of accessing data on-line.3. Mostly of the existing methods for modeling and simulation biological systems only fit to simple biological process, so modeling and simulation techniques and methods need further develop. In this dissertation, we propose an agent-based modeling method at molecular scale (ABMMS) and construct a computation platform based on agent technology to analyze biological networks. We can study their complex macro phenomenon emerge from behaviors of agents. It provides a new way to study and understand biological systems, which can reveal internal mechanisms of biological systems and the relation between complex macro phenomenon and molecular behaviors at micro.4. Usually, the actual biological systems with the very high complexity, we need the higher performance of computation for modeling and simulation biochemical network. Based on agent technology and parallel theory this dissertation proposes a new distributed-based stochastic simulation algorithms (DSSA) us(?) ing multi-agents system and distributed computing to improve computing performance SSA. DSSA mainly through decomposed SSA into the framework of based on distributed multi-agent system, and through reaction relationship to further reduce the cost of computing and communications. Experiments showed DSSA algorithm is able to improve time performance significantly, especially for some large-scale biochemical networks.The application of our method, studying the biochemical system through the multiple source data integration and agent-based modeling method, not only has great theoretical value, but also has broad application prospects. We has done some research work under the framework of study of biochemical network system by multi-source data integration and modeling and simulation. And our future directions are how to further improve the existing methods and platform system for the study of the evolution mechanism of biological systems.